The field of this disclosure relates generally to systems and methods of identifying objects, and more particularly, but not exclusively, to data reading (e.g., reading optical codes) and object recognition.
Various approaches have been used to identify objects, such as items or merchandise, in retail applications. One approach involves the use of optical codes that encode useful, optically-readable information about the objects to which they are attached or otherwise associated. An optical code is essentially a machine-readable representation of information in a visual format. Some optical codes use a dark ink on a white substrate to create high and low reflectance upon scanning or reading of the optical code. For the purposes of the present description, the terms scanning and reading may be used interchangeably to connote acquiring data associated with an optical code. Likewise, optical code scanners and optical code readers may be used interchangeably to connote devices used to acquire (e.g., scan or read) data associated with an optical code.
Optical codes have various symbologies, such as UPC, Code 39, Code 128, and PDF417, that encode information data characters (or codewords as in the case of PDF417) and optional overhead characters according to a particular sequence of bars and spaces that may have varying widths. Perhaps the best example of an optical code is the barcode. Barcodes are ubiquitously found on or associated with objects of various types, such as the packaging of retail, wholesale, and inventory goods; retail product presentation fixtures (e.g., shelves); goods undergoing manufacturing; personal or company assets; and documents. By encoding information, a barcode typically serves as an identifier of an object, whether the identification be to a class of objects (e.g., containers of milk) or a unique item.
Various types of optical code readers, such as manual readers, semi-automatic readers, and automated readers, are available to decode the information encoded in optical codes. In a manual reader (e.g., a hand-held type reader, a fixed-position reader), a human operator positions a target object relative to the reader to read the optical code associated with the target object. In a semi-automatic reader, either checker-assisted or self-checkout, objects are moved usually one at a time by the user into or through a read zone of the reader and the reader then reads the optical code on the object. In an automated reader (e.g., a tunnel scanner, a portal scanner), the target object is automatically positioned (e.g., via a conveyor) relative to the reader to read the optical code on the target object. In manual, semi-automatic, and automated readers, an error (e.g., a no-code exception) may occur when the reader attempts to decode the optical code on the target object. For example, the reader may be unsuccessful in decoding the entire optical code of the target object because part of the optical code is blocked or otherwise obscured from the view of the reader. When such an error occurs, an operator may have to manually enter (e.g., via a keyboard) an identification number (e.g., a UPC number) or description of the target object to account for it in a transaction.
Another approach to identifying objects implements visual object recognition. Prior to recognizing a target object in a typical visual object recognition system, a database is built that includes visual features extracted from a plurality of known objects that one wants to recognize. To recognize the target object, sets of features are extracted from an image of the target object and the database is searched for the most similar feature to each feature in the set of extracted features (called a nearest neighbor). The nearest-neighbor matches from the database identify the known object to which the target object corresponds.